Businesses today are under intense pressure to compete in an environment of tight deadlines and reduced profits. One key to being successful in this environment is having timely and accurate financial and other business performance data that reflects the state of the corporation. It would be difficult for a modern large enterprise to be successful without accurate gathering and analysis of financial and other business performance data.
Businesses rely on financial data in order to support decision-making. The financial data is maintained in computerized financial reporting systems. For some large entities, these reporting systems process large numbers of complex transactions occurring at locations around the world. Businesses attempt to use this data to determine some behavior, such as predicted end-of-month revenue, for supporting business decisions. However, modeling the complex financial transactions of the large enterprise is very difficult.
Traditionally, business enterprise data has been kept in databases that are sometimes specialized and often separate from other data repositories. Data may be stored in various incompatible databases and formats across corporate divisions. A major task in managing the large enterprise is effectively gathering this data into repositories for analysis within various levels of the organization.
Recently, businesses have started exploring the feasibility of applying traditional statistical analysis techniques to large databases for the purpose of discovering hidden data attributes, trends, and patterns. This exploration, known as data mining, has evolved into the creation of analytical tools based on a wide collection of statistical techniques.
For a corporation, the discovery of previously unknown statistical patterns or trends can provide valuable insight into the function and environment of their organization. Data-mining techniques allow businesses to make predictions of future events, whereas analysis of warehoused data only gives evidence of past facts.
A common approach to analyzing this data is to have a human expert extract, sort, and process important parts of the data for trend analysis and forecasting. This method can be effective, but is rather slow and highly dependent on the skill of the analyst. Although the use of computers makes arranging and viewing the date much more convenient, traditional computing operations still require human interaction to spot trends in order to provide acceptable results on which to base important business decisions.
Due to ever shortening business cycles and the need to distribute information to all parts of the enterprise, legacy business processes that require data to be extracted and manually manipulated before use will be much less desirable. Instead, enterprises will need rapid decision support based on rapid analysis and forecasting of future behavior.
A system and method that address the aforementioned problems, as well as other related problems, are therefore desirable.